Infrared (IR) imaging facilitates viewing the relative thermal intensity of objects in a scene. IR imaging may include, for example, long wave IR (e.g., 7-14 microns), mid-wave IR (e.g., 3-5 microns), near IR (e.g., 0.7-2 microns), and so on.
IR imaging systems include a thermal imager. A thermal imager may include a detector that includes an arrangement (e.g., two-dimensional array) of individual IR sensors. The IR sensors may be configured to digitize an analog thermal intensity signal into a discrete value. The detector may be configured to repeatedly sample a scene at a sampling rate. The imaging system may be configured to provide images at a video refresh rate. So long as the sample rate exceeds the video refresh rate, meaningful real-time imagery may be possible. Manual integration value manipulation and post-acquisition image reconstruction may make real-time imagery difficult, if possible at all.
During signal acquisition, infrared measurements are collected for a period of time referred to as the integration time or the integration period. Longer integration times facilitate viewing less thermally intense objects while shorter integration times facilitate viewing more thermally intense objects. Scenes having both less thermally intense objects and more thermally intense objects may complicate selecting an appropriate integration time.
To understand the integration time selection issue, consider that a long wave IR sensor can only measure values in a fixed range. For example, a 12 bit IR sensor can digitize thermal intensity analog signals into 212 discrete values (e.g., 0 to 4095) while a 16 bit IR sensor can digitize signals into 216 (e.g., 0 to 65,535) discrete values. Within its fixed range, an IR sensor may exhibit different response patterns. For example, near the center of the fixed range the IR sensor may exhibit a linear response while near the ends of the fixed range the IR sensor may not exhibit a linear response. This linear versus non-linear response can affect the quality of images produced from the digitized signals, particularly when a scene includes objects exhibiting a wide range of temperatures. It is to be appreciated that IR sensors in other ranges may have similar issues. Similarly, it is to be appreciated that other sensors (e.g., optical sensors) may exhibit similar traits.
Conventionally, a single integration time may be selected and employed based on an overall (e.g., average) thermal intensity of a scene. However, this approach may provide sub-optimal results since some objects may not provide enough thermal radiation for meaningful acquisition while others may provide so much thermal radiation they overwhelm the detectors. Shortening the integration time to account for warm objects may cause colder objects to “disappear” while lengthening the integration time to account for cold objects may cause warmer objects to be over-exposed. In either case scene detail may be lost.
To address this integration time selection issue, some conventional systems have employed manually controlled cyclic integration. Cyclic integration involves imaging a scene multiple times using different integration times for various images. Conventional systems may require a user to manually select the integration time. The separate images acquired with different integration times may then be reconstructed into a single image. By way of illustration, a first image may be acquired at a first time using a first user-selected integration time, a second image may be acquired at a second time using a second user-selected integration time, and a third image may be acquired at a third time using a third user-selected integration time. The three integration times may account for different thermal intensities for different objects in a scene. In other applications, the three integration times may account for different optical intensities for different objects in a scene. The three images may then be reconstructed into a single image that includes the most useful data from each of the three images. The most useful data may be selected, for example, using intensity thresh-holding criteria.
Reconstruction for manually controlled cyclic integration systems is conventionally performed in a post-processing action. The post-processing step can make the reconstructed images substantially worthless for real-time surveillance and/or targeting applications due to time lag. To employ post-processed images in real-time applications would require detectors with an unusually high sampling rate that is sufficient to acquire images with each of the different integration times in less than the output video refresh period. Additionally, the post-processing system would be required to assemble the images in a period of time less than the refresh period minus the accumulated sampling time. This type of system would likely be prohibitively expensive. Even if such a system could be built, it would likely require a-priori knowledge of useful integration times and would also likely require manual intervention to adjust integration times in response to changing scenes. These constraints are unacceptable in many real-time surveillance and/or targeting applications.